Is accuracy of a machine learning algorithm independent of the features?
This is a result of overfitting, I think. Your model is allowed to explain the phenomenon using any combination of variables, whether or not such a  combination constitutes a true signal.

I am not familiar with the details of a NBC, but I suspect that the model has some bias/offset/constant variable, that can be combined with your input features.

Thusly, what you see is just an expression that  the classifier has been able to combine each set of features and the constant variables in such a way that they perfectly describe the phenomenon. These combinations will be non-linear, so the logic as to how dependent the model is on any parameter is not a simple "it follows"-reasoning.

However, if you evaluate OUT of sample, you will see a much better reflection of how useful individual features are, and how bad the performance becomes when you remove the first three features.

For example, do a K-folds test (say, K=10) of the six different models, preserving 20% of the dataset as test-data.

Here you would likely see the following rankings:

(4, 5, 6, 1 ~ equal) > 2 > 3